# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os

import numpy as np
import safetensors
import torch

VAE_KEY_MAPPING = {
    "encoder.conv_in.weight": "first_stage_model.encoder.conv_in.weight",
    "encoder.conv_in.bias": "first_stage_model.encoder.conv_in.bias",
    "encoder.conv_out.weight": "first_stage_model.encoder.conv_out.weight",
    "encoder.conv_out.bias": "first_stage_model.encoder.conv_out.bias",
    "encoder.conv_norm_out.weight": "first_stage_model.encoder.norm_out.weight",
    "encoder.conv_norm_out.bias": "first_stage_model.encoder.norm_out.bias",
    "decoder.conv_in.weight": "first_stage_model.decoder.conv_in.weight",
    "decoder.conv_in.bias": "first_stage_model.decoder.conv_in.bias",
    "decoder.conv_out.weight": "first_stage_model.decoder.conv_out.weight",
    "decoder.conv_out.bias": "first_stage_model.decoder.conv_out.bias",
    "decoder.conv_norm_out.weight": "first_stage_model.decoder.norm_out.weight",
    "decoder.conv_norm_out.bias": "first_stage_model.decoder.norm_out.bias",
    "quant_conv.weight": "first_stage_model.quant_conv.weight",
    "quant_conv.bias": "first_stage_model.quant_conv.bias",
    "post_quant_conv.weight": "first_stage_model.post_quant_conv.weight",
    "post_quant_conv.bias": "first_stage_model.post_quant_conv.bias",
    "encoder.down_blocks.0.downsamplers.0.conv.weight": "first_stage_model.encoder.down.0.downsample.conv.weight",
    "encoder.down_blocks.0.downsamplers.0.conv.bias": "first_stage_model.encoder.down.0.downsample.conv.bias",
    "encoder.down_blocks.0.resnets.0.conv1.bias": "first_stage_model.encoder.down.0.block.0.conv1.bias",
    "encoder.down_blocks.0.resnets.0.conv1.weight": "first_stage_model.encoder.down.0.block.0.conv1.weight",
    "encoder.down_blocks.0.resnets.0.conv2.bias": "first_stage_model.encoder.down.0.block.0.conv2.bias",
    "encoder.down_blocks.0.resnets.0.conv2.weight": "first_stage_model.encoder.down.0.block.0.conv2.weight",
    "encoder.down_blocks.0.resnets.0.norm1.bias": "first_stage_model.encoder.down.0.block.0.norm1.bias",
    "encoder.down_blocks.0.resnets.0.norm1.weight": "first_stage_model.encoder.down.0.block.0.norm1.weight",
    "encoder.down_blocks.0.resnets.0.norm2.bias": "first_stage_model.encoder.down.0.block.0.norm2.bias",
    "encoder.down_blocks.0.resnets.0.norm2.weight": "first_stage_model.encoder.down.0.block.0.norm2.weight",
    "encoder.down_blocks.0.resnets.1.conv1.bias": "first_stage_model.encoder.down.0.block.1.conv1.bias",
    "encoder.down_blocks.0.resnets.1.conv1.weight": "first_stage_model.encoder.down.0.block.1.conv1.weight",
    "encoder.down_blocks.0.resnets.1.conv2.bias": "first_stage_model.encoder.down.0.block.1.conv2.bias",
    "encoder.down_blocks.0.resnets.1.conv2.weight": "first_stage_model.encoder.down.0.block.1.conv2.weight",
    "encoder.down_blocks.0.resnets.1.norm1.bias": "first_stage_model.encoder.down.0.block.1.norm1.bias",
    "encoder.down_blocks.0.resnets.1.norm1.weight": "first_stage_model.encoder.down.0.block.1.norm1.weight",
    "encoder.down_blocks.0.resnets.1.norm2.bias": "first_stage_model.encoder.down.0.block.1.norm2.bias",
    "encoder.down_blocks.0.resnets.1.norm2.weight": "first_stage_model.encoder.down.0.block.1.norm2.weight",
    "encoder.down_blocks.1.downsamplers.0.conv.weight": "first_stage_model.encoder.down.1.downsample.conv.weight",
    "encoder.down_blocks.1.downsamplers.0.conv.bias": "first_stage_model.encoder.down.1.downsample.conv.bias",
    "encoder.down_blocks.1.resnets.0.conv1.bias": "first_stage_model.encoder.down.1.block.0.conv1.bias",
    "encoder.down_blocks.1.resnets.0.conv1.weight": "first_stage_model.encoder.down.1.block.0.conv1.weight",
    "encoder.down_blocks.1.resnets.0.conv2.bias": "first_stage_model.encoder.down.1.block.0.conv2.bias",
    "encoder.down_blocks.1.resnets.0.conv2.weight": "first_stage_model.encoder.down.1.block.0.conv2.weight",
    "encoder.down_blocks.1.resnets.0.conv_shortcut.bias": "first_stage_model.encoder.down.1.block.0.nin_shortcut.bias",
    "encoder.down_blocks.1.resnets.0.conv_shortcut.weight": "first_stage_model.encoder.down.1.block.0.nin_shortcut.weight",
    "encoder.down_blocks.1.resnets.0.norm1.bias": "first_stage_model.encoder.down.1.block.0.norm1.bias",
    "encoder.down_blocks.1.resnets.0.norm1.weight": "first_stage_model.encoder.down.1.block.0.norm1.weight",
    "encoder.down_blocks.1.resnets.0.norm2.bias": "first_stage_model.encoder.down.1.block.0.norm2.bias",
    "encoder.down_blocks.1.resnets.0.norm2.weight": "first_stage_model.encoder.down.1.block.0.norm2.weight",
    "encoder.down_blocks.1.resnets.1.conv1.bias": "first_stage_model.encoder.down.1.block.1.conv1.bias",
    "encoder.down_blocks.1.resnets.1.conv1.weight": "first_stage_model.encoder.down.1.block.1.conv1.weight",
    "encoder.down_blocks.1.resnets.1.conv2.bias": "first_stage_model.encoder.down.1.block.1.conv2.bias",
    "encoder.down_blocks.1.resnets.1.conv2.weight": "first_stage_model.encoder.down.1.block.1.conv2.weight",
    "encoder.down_blocks.1.resnets.1.norm1.bias": "first_stage_model.encoder.down.1.block.1.norm1.bias",
    "encoder.down_blocks.1.resnets.1.norm1.weight": "first_stage_model.encoder.down.1.block.1.norm1.weight",
    "encoder.down_blocks.1.resnets.1.norm2.bias": "first_stage_model.encoder.down.1.block.1.norm2.bias",
    "encoder.down_blocks.1.resnets.1.norm2.weight": "first_stage_model.encoder.down.1.block.1.norm2.weight",
    "encoder.down_blocks.2.downsamplers.0.conv.weight": "first_stage_model.encoder.down.2.downsample.conv.weight",
    "encoder.down_blocks.2.downsamplers.0.conv.bias": "first_stage_model.encoder.down.2.downsample.conv.bias",
    "encoder.down_blocks.2.resnets.0.conv1.bias": "first_stage_model.encoder.down.2.block.0.conv1.bias",
    "encoder.down_blocks.2.resnets.0.conv1.weight": "first_stage_model.encoder.down.2.block.0.conv1.weight",
    "encoder.down_blocks.2.resnets.0.conv2.bias": "first_stage_model.encoder.down.2.block.0.conv2.bias",
    "encoder.down_blocks.2.resnets.0.conv2.weight": "first_stage_model.encoder.down.2.block.0.conv2.weight",
    "encoder.down_blocks.2.resnets.0.conv_shortcut.bias": "first_stage_model.encoder.down.2.block.0.nin_shortcut.bias",
    "encoder.down_blocks.2.resnets.0.conv_shortcut.weight": "first_stage_model.encoder.down.2.block.0.nin_shortcut.weight",
    "encoder.down_blocks.2.resnets.0.norm1.bias": "first_stage_model.encoder.down.2.block.0.norm1.bias",
    "encoder.down_blocks.2.resnets.0.norm1.weight": "first_stage_model.encoder.down.2.block.0.norm1.weight",
    "encoder.down_blocks.2.resnets.0.norm2.bias": "first_stage_model.encoder.down.2.block.0.norm2.bias",
    "encoder.down_blocks.2.resnets.0.norm2.weight": "first_stage_model.encoder.down.2.block.0.norm2.weight",
    "encoder.down_blocks.2.resnets.1.conv1.bias": "first_stage_model.encoder.down.2.block.1.conv1.bias",
    "encoder.down_blocks.2.resnets.1.conv1.weight": "first_stage_model.encoder.down.2.block.1.conv1.weight",
    "encoder.down_blocks.2.resnets.1.conv2.bias": "first_stage_model.encoder.down.2.block.1.conv2.bias",
    "encoder.down_blocks.2.resnets.1.conv2.weight": "first_stage_model.encoder.down.2.block.1.conv2.weight",
    "encoder.down_blocks.2.resnets.1.norm1.bias": "first_stage_model.encoder.down.2.block.1.norm1.bias",
    "encoder.down_blocks.2.resnets.1.norm1.weight": "first_stage_model.encoder.down.2.block.1.norm1.weight",
    "encoder.down_blocks.2.resnets.1.norm2.bias": "first_stage_model.encoder.down.2.block.1.norm2.bias",
    "encoder.down_blocks.2.resnets.1.norm2.weight": "first_stage_model.encoder.down.2.block.1.norm2.weight",
    "encoder.down_blocks.3.resnets.0.conv1.bias": "first_stage_model.encoder.down.3.block.0.conv1.bias",
    "encoder.down_blocks.3.resnets.0.conv1.weight": "first_stage_model.encoder.down.3.block.0.conv1.weight",
    "encoder.down_blocks.3.resnets.0.conv2.bias": "first_stage_model.encoder.down.3.block.0.conv2.bias",
    "encoder.down_blocks.3.resnets.0.conv2.weight": "first_stage_model.encoder.down.3.block.0.conv2.weight",
    "encoder.down_blocks.3.resnets.0.norm1.bias": "first_stage_model.encoder.down.3.block.0.norm1.bias",
    "encoder.down_blocks.3.resnets.0.norm1.weight": "first_stage_model.encoder.down.3.block.0.norm1.weight",
    "encoder.down_blocks.3.resnets.0.norm2.bias": "first_stage_model.encoder.down.3.block.0.norm2.bias",
    "encoder.down_blocks.3.resnets.0.norm2.weight": "first_stage_model.encoder.down.3.block.0.norm2.weight",
    "encoder.down_blocks.3.resnets.1.conv1.bias": "first_stage_model.encoder.down.3.block.1.conv1.bias",
    "encoder.down_blocks.3.resnets.1.conv1.weight": "first_stage_model.encoder.down.3.block.1.conv1.weight",
    "encoder.down_blocks.3.resnets.1.conv2.bias": "first_stage_model.encoder.down.3.block.1.conv2.bias",
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    "encoder.down_blocks.3.resnets.1.norm1.weight": "first_stage_model.encoder.down.3.block.1.norm1.weight",
    "encoder.down_blocks.3.resnets.1.norm2.bias": "first_stage_model.encoder.down.3.block.1.norm2.bias",
    "encoder.down_blocks.3.resnets.1.norm2.weight": "first_stage_model.encoder.down.3.block.1.norm2.weight",
    "encoder.mid_block.resnets.0.conv1.bias": "first_stage_model.encoder.mid.block_1.conv1.bias",
    "encoder.mid_block.resnets.0.conv1.weight": "first_stage_model.encoder.mid.block_1.conv1.weight",
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    "encoder.mid_block.resnets.0.norm2.weight": "first_stage_model.encoder.mid.block_1.norm2.weight",
    "encoder.mid_block.resnets.1.conv1.bias": "first_stage_model.encoder.mid.block_2.conv1.bias",
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    "encoder.mid_block.resnets.1.conv2.bias": "first_stage_model.encoder.mid.block_2.conv2.bias",
    "encoder.mid_block.resnets.1.conv2.weight": "first_stage_model.encoder.mid.block_2.conv2.weight",
    "encoder.mid_block.resnets.1.norm1.bias": "first_stage_model.encoder.mid.block_2.norm1.bias",
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    "encoder.mid_block.attentions.0.to_k.bias": "first_stage_model.encoder.mid.attn_1.k.bias",
    "encoder.mid_block.attentions.0.to_k.weight": "first_stage_model.encoder.mid.attn_1.k.weight",
    "encoder.mid_block.attentions.0.group_norm.bias": "first_stage_model.encoder.mid.attn_1.norm.bias",
    "encoder.mid_block.attentions.0.group_norm.weight": "first_stage_model.encoder.mid.attn_1.norm.weight",
    "encoder.mid_block.attentions.0.to_out.0.bias": "first_stage_model.encoder.mid.attn_1.proj_out.bias",
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    "encoder.mid_block.attentions.0.to_v.bias": "first_stage_model.encoder.mid.attn_1.v.bias",
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CKPT_MAPPING = {
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        ('encoder.down_blocks.0.resnets.0.conv1.bias', None),
        ('encoder.down_blocks.0.resnets.0.norm2.weight', None),
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        (f"model.diffusion_model.output_blocks.8.0.out_layers.3.weight", (2, 3, 1, 0)),
        (f"model.diffusion_model.output_blocks.8.0.out_layers.3.bias", None),
        (f"model.diffusion_model.output_blocks.8.0.skip_connection.weight", (2, 3, 1, 0)),
        (f"model.diffusion_model.output_blocks.8.0.skip_connection.bias", None),
        (f"model.diffusion_model.out.0.weight", None),
        (f"model.diffusion_model.out.0.bias", None),
        (f"model.diffusion_model.out.2.weight", (2, 3, 1, 0)),
        (f"model.diffusion_model.out.2.bias", None), ]
}


def load_weights_from_file(self, ckpt_path, ckpt_mapping, key_mapping=None, lora_dict=None):
    filename = os.path.basename(ckpt_path)
    print("{} loading:[{}]".format(self.name, filename))
    state_dict = {}
    if ckpt_path.endswith(".safetensors"):
        with safetensors.safe_open(ckpt_path, framework="pt", device="cpu") as f:
            for key in f.keys():
                state_dict[key] = f.get_tensor(key)
    else:
        state_dict = torch.load(ckpt_path, map_location="cpu")
    print("loaded :[{}]".format(filename))
    weights = self.weights
    module_weights = []
    keys = state_dict.keys()
    if lora_dict is not None:
        lora_keys = lora_dict.keys()
    else:
        lora_keys = []
    lora_count = len(lora_keys)
    lora_idx = 0
    for i, (key, perm) in enumerate(ckpt_mapping):
        if isinstance(perm, str) and perm.endswith(".npy"):
            w = np.load(perm)
        else:
            if key_mapping is not None:
                if key in keys:
                    w = state_dict[key]
                else:
                    w = state_dict[key_mapping[key]]
            else:
                w = state_dict[key]
            if isinstance(w, torch.Tensor):
                w = w.detach().numpy()
            if lora_dict is not None:
                if key_mapping is not None:
                    lora_w = lora_dict.get(str(key_mapping.get(key, None)), None)
                else:
                    lora_w = lora_dict.get(key, None)
                if lora_w is not None:
                    w = w + lora_w
                    lora_idx += 1
            if perm is not None:
                w = np.transpose(w, perm)
        if weights[i].shape != w.shape:
            print("Wrong :[{},{}]".format(weights[i].name, key))
        module_weights.append(w)
    if lora_count > 0:
        if lora_idx == lora_count:
            print("Apply {} lora weights".format(lora_count))
        else:
            print("Apply {}/{} lora weights".format(lora_idx, lora_count))
    self.set_weights(module_weights)
    print("Loaded %d weights for %s" % (len(module_weights), self.name))
